Detecting Latent Topics for Categorizing Educational Dissertations of Leading Universities in the United Staes and Thailand: Structural Topic Modeling Analysis
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Abstract
This research aimed to conduct a rigorous examination and comparison of latent topics of educational dissertations in leading universities in Thailand and the U.S. The dataset was acquired through web scraping, encompassing titles and abstracts of educational dissertations spanning a five-year period from 2019 to 2023. Specifically, we gathered 435 subjects in Thai from the Chulalongkorn University Institutional Repository (CUIR) and 363 subjects in English from the databases of the top 10 U.S. universities. The dataset was meticulously curated and processed utilizing Python's Natural Language Toolkit (NLTK) library. Subsequently, we performed an in-depth analysis employing Topic Modeling Analysis, with a focus on the Latent Dirichlet Allocation (LDA) method, BERTopic library, and Uniform Manifold Approximation and Projection (UMAP) model. The findings of this research underscore significant disparities in the topics explored within educational dissertations between Thailand and top-tier U.S. universities. In the context of Thailand, dissertation topics predominantly revolved around the development of teaching methodologies, student learning enhancement, and the resolution of teacher development issues. In contrast, dissertations from U.S. institutions encompassed a broader spectrum of themes, including area-based education, the development of ethnic and racial identity in students, and strategies for promoting equality among learners with diverse backgrounds. These outcomes highlight noteworthy variations in the academic emphasis and research priorities of these two distinct educational landscapes.
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